This invention relates to a process for pattern detection in static or dynamic systems.
The systems contemplated herein encompass, in the broadest sense, all physical, chemical or bio-medical processes or materials with a state or individual features which can be characterized with a set of n parameters. The systems can be invariable (static) or variable over time (dynamic) in the study interval. In the case of dynamic systems, time is one of n parameters.
The characterizing parameters are formed either by system-imminent or by externally induced physical or technical features. They can comprise especially geometric arrangements, time arrangements or amplitude variations of certain physical or technical quantities.
Various processes for recognizing structures (patterns) in n-dimensional spaces are known. They include, for example, local search processes for density fluctuations, the so-called maximum entropy process and the so-called maximum likelihood process. However, these known processes require assumptions or prior information to be able to identify structures or patterns. Another deficiency of known methods is that expansion to more than two dimensions is very computer-intensive, that nonuniform measured quantities and many different correlations cannot be processed, and that the sensitivity in irregular patterns with strong uncorrelated disruptions is low. Furthermore, the publication of H. Atmanspacher et al. (PHYSICAL REVIEW A, vol. 40, no. 7, October 1989, USA, pages 3954-3963 "Determination of F(alpha) for a Limited Random Point Set (Galaxy Distribution)") discloses a process in which, by determining the F spectrum in a predominantly uncorrelated set of points, possibly present correlated subsets can be identified.
Another process is known from the publication of H. Ebeling et al. (PHYSICAL REVIEW E, vol. 47, no. 1, January 1993, USA, pages 704-710 "Detecting Structure in Two Dimensions Combining Voronoi Tessellation and Percolation"). In this process, the original data point field is divided into individual cells and the cell distribution is compared to the one which would have been expected in a statistical Poisson distribution.
A simplified and more reliable approach to pattern recognition in n-dimensional space is the space filter process described in DE-OS-43 17 746. Here, the system state is represented by a point distribution in the state space (n dimensions). The change of point density (gradient) around a studied point is described by isotropic scaling factor a, which is a measure for the surrounding number of points depending on the distance from the studied point. According to the known process, the difference of the frequency distribution of all a-values of the studied points and the frequency distribution of the a values of a reference state is used to recognize local density fluctuations.
However, this known space filter process has the following defects. Using the process, structures can only be detected in the state space without the possibility of classifying its orientation. Thus, comparison with predetermined searched structures and, thus, location or signalling of certain states in state space are not possible either. This process is therefore limited to applications especially in image processing. Moreover, structure recognition according to the known process can be inaccurate in cases in which local density fluctuations are "smeared" over spatial areas which are much larger than the environment considered in the study of one point. Measurement and recognition of these blurred edges of image structures are occasionally faulty, so that a subsequent indication of pattern occurrence can be unreliable.
Finally, conventional procedures for detecting system states, especially for recognizing faulty structures, are known in systems in which the existence of a fault is detected by a global process and is then located by local studies. This is frequently impractically time consuming. This especially applies in applications for state detection of a host of systems, for example, in mass production.